Photonic AI for deeper understanding of the Planck epoch

Abstract

This article addresses the requirements for a photonic artificial intelligence (AI) capable of supporting a deeper understanding of the Planck epoch in the origin of the Universe. The article continues the previous author’s articles, which outlined requirements for AI to support studies of protein evolution on Earth, cosmic strings (CS) detection, and a non-standard view on the photon’s nature. Modern digital generative AI cannot support its solution due to a lack of training datasets and limited energy resources for conducting adequate physical experiments. This article analyses the states of the Planck epoch, when there was no space–time, and when entropy began to emerge. It outlines additional requirements for an AI and proposes an adequate full-analogue photonic AI (PAI) that can be helpful for the research. The main idea of the PAI is to reject digitising the natural analogue signal and to use single-step Fourier convolution on many images simultaneously for representing states of the Planck epoch. To create PAI, a single holographic plate must replace a multilevel digital neural network, thereby eliminating the need for traditional multi-step machine learning. PAI is also expected to provide effective solutions to complex tasks such as Markov chains, genetic algorithms, and Kolmogorov (Fokker-Planck) equations, which are necessary for this research. The author’s convergent methodology helps make the research focused and stable. This methodology is based on fundamental thermodynamics and the inverse-problem-solving method in topological spaces. The PAI project has a starting technology readiness level: methodology, principles, and design.